1. Field of Invention
The present invention relates to a method for screening samples for building a prediction model and a computer program product thereof. More particularly, the present invention relates to a method for screening samples for building a prediction model by using a dynamic moving window (DMW) and a computer program product thereof.
2. Description of Related Art
Historical sample data are often used for building a first set of prediction models, and the prediction models are applied in an actual process environment. When a set of sample data is newly collected, a conventional skill uses the newly-collected sample data for model refreshing, so as to refresh or retrain the prediction models. Therefore, the prediction accuracy of the prediction models is closely related to the historical sample data and the new sample data collected during an on-line model-refreshing phase.
In an example of building a virtual metrology (VM) model, a process tool will confront the events such as equipment preventive maintenance, equipment drift or design of experiments (DOE) required for equipment feature tests or calibration. If important sample data can be collected for the aforementioned events, the data features contained in the CM model will be more complete, thereby promoting the accuracy and stability of real-time conjecturing product quality by virtual metrology. Typically, a conventional skill adopts a static moving window (SMW) scheme to keep and administer sample data for model building. The so-called “SMW” scheme uses a set of sample data newly entering a window to replace the oldest sample data in the window with a fixed total number of sample data. Thus, with the advance of time, the sample data at the front end of the window will be gradually replaced by the sample data subsequently entering the window, such that the important sample data related to the aforementioned events will be discarded out of the model-building samples. Therefore, when a similar equipment drift event occurs again, the prediction model (VM model) will fail to accurately conjecture such an equipment drift event.
Hence, there is a need to provide a method for screening samples for building a prediction model and a computer program product thereof for obtaining and keeping enough important model-building sample data to overcome the aforementioned disadvantages of the conventional skill.